In retail stores, fixtures are arranged in a way that enhances visual merchandising, in order to improve sales and profits for a particular brand or company. Thus compliance at brand, product, and category level plays an important role in overall profits. Companies pay for prime spaces on fixtures and major share of shelf space. These are of huge importance for different competing companies and thus they spend huge money for planogram compliance supervisions. Representatives from such companies visit stores to manually supervise compliance. However, manual supervision is not only expensive, it is time taking as well.
Some conventional systems use mobile cameras or CCTV camera to capture images in order to automatically process and analyze various Key Performance Indicators (KPIs) to check planogram compliance. Product recognition plays an important role in computing these KPI's. However, nowadays product wrappers and packet colors change quite frequently along with new variants being introduced on regular basis to counter rivals. Thus, in order to recognize a product, it is important to first accurately determine products' region of interest, i.e., the region where products are placed on a fixture to enable visual merchandizing.
Some conventional systems provide for detecting a product region using pre-defined fixture information. However, this method is not a scalable solution since it pre-defined fixture information has to be captured for different stores. It is not feasible to manually calculate and store fixture information for all kinds of fixtures in different stores. In addition, this method is not dynamic in nature and requires an immediate update, whenever a new fixture is added in a given store.